TY  - JA
AU  - JULIANA P.
AU  - Montesinos-Lopez,O.A.
AU  - Crossa,J.
AU  - Mondal,S.
AU  - Gonzalez-Perez,L.
AU  - Poland,J.A.
AU  - Huerta-Espino,J.
AU  - Crespo-Herrera,L.A.
AU  - Velu,G.
AU  - Dreisigacker,S.
AU  - Shrestha,S.
AU  - Perez-Rodriguez,P.
AU  - Pinto Espinosa,F.
AU  - Singh,R.P.
TI  - Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat
PY  - 2019///
CY  - Germany : 
PB  - Springer, 
KW  - AGROVOC
KW  - Genomics
KW  - Phenotypes
KW  - Breeding
KW  - Climatic factors
KW  - Soft wheat
KW  - Climate change
KW  - Resilience
N1  - Peer review; Open Access; WC; FP3
N2  - Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center?s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress?resilience within years
UR  - https://hdl.handle.net/10883/19787
DO  - https://doi.org/10.1007/s00122-018-3206-3
T2  - Theoretical and Applied Genetics
ER  -